CD Skripsi

Prediksi Respons Struktur Bangunan Berdasarkan Spektra Gempa Indonesia Di Pulau Sumatera Menggunakan Jaringan Saraf Tiruan

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Artificial neural network (ANN) is a mathematical model developed as the counterfeit of its biological neural network. ANN is capable of modeling nonlinear relationship between specified input and output parameters. In civil engineering field, ANN has been widely used as a prediction tool especially for structural analysis. In this research, ANN is utilized to predict structural response of highrise reinforced concrete building as a function of seismic load parameters, soil condition, and building geometry. Building structural response data are generated by performing modal response spectrum analysis based on 11 seismic locations in Sumatera Island, 3 soil conditions, and 3 building models (10 story, 15 story, and 20 story), aided by finite element computer program to do the tedious computations. These variations resulted in 1485 structural response data sets (for story-drift, story-velocity, and story-acceleration) which are further evaluated based on SNI 1726-2012. As much as 1080 data sets are fed into the ANN for training process, whereas the remaining 405 data sets are used for testing process. After training, the ANN predicts story-drift and story-velocity at the accuracy of 95%. However for story-acceleration, the prediction accuracy is lower (83%). Using the trained ANN weight factors, an ANN based software is created and has the ability to predict story-drift, story-velocity, and story-acceleration of reinforced concrete building (specific for the model in this research) subject to seismic loading in Sumatera Island.
Keywords: artificial neural network (ANN), modal response spectrum analysis, _seismic/earthquake

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Ketersediaan

Informasi Detail

Judul Seri

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No. Panggil

07 01. 115 (0040)

Penerbit

Pekanbaru : Universitas Riau - Fakultas Teknik - Teknik Sipil., 2015

Deskripsi Fisik

xv, 95 hlm.:ill,; 29 cm

Bahasa

ISBN/ISSN

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Klasifikasi

07 01. 115 (0040)

Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

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Subjek

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Info Detail Spesifik

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Pernyataan Tanggungjawab

DAUS

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